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Dataset assetOpen Source CommunityMedical Image SegmentationCardiac Ultrasound

CAMUS_public-ImageMask-Dataset

This is a CAMUS_public dataset for image segmentation (Cardiac Multi‑structure Ultrasound Segmentation). It contains clinical examinations from 500 patients performed at the University Hospital of Saint‑Étienne, France, fully anonymized according to local ethics committee regulations. The dataset is intended for left‑ventricular ejection fraction measurement and reflects clinical variability in image quality and pathology. It is split into a training set (450 patients) and a test set (50 new patients). Raw input images are provided in .raw/.mhd format.

Source
github
Created
Feb 5, 2024
Updated
Feb 5, 2024
Signals
1,270 views
Availability
Linked source ready
Overview

Dataset description and usage context

Dataset Overview

Dataset Name

CAMUS_public-ImageMask-Dataset

Dataset Description

CAMUS_public (Cardiac Acquisitions for Multi‑structure Ultrasound Segmentation) is an image‑segmentation dataset comprising clinical examinations from 500 patients collected at the University Hospital of Saint‑Étienne, France, fully anonymized according to the hospital ethics committee. The dataset targets left‑ventricular ejection fraction measurement without any pre‑selection, preserving clinical diversity.

Dataset Composition

  • Training set: 450 patients with expert‑annotated references.
  • Test set: 50 new patients.
  • File format: Raw input images are supplied as .raw/.mhd files.

Dataset Structure

  • Raw dataset layout:

    ./CAMUS_public ├─database_nifti │ ├─patient0001 │ ├─patient0002 │ … │ └─patient0500 └─jupyter

    Each patient folder contains two file types: *.nii.gz (image) and _gt.nii.gz (mask).

  • Processed dataset layout:

    ../CAMUS_public-ImageMask-Dataset ├─test │ ├─images │ └─masks ├─train │ ├─images │ └─masks └─valid ├─images └─masks

Dataset Download

Citation

  • Paper: S. Leclerc, E. Smistad, J. Pedrosa, A. Ostvik, et al. "Deep Learning for Segmentation using an Open Large‑Scale Dataset in 2D Echocardiography" in IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2198‑2210, Sept. 2019.
  • DOI: 10.1109/TMI.2019.2900516
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